With the progress of science and technology as well as increasing demands in traffic and transportation, the number of bridges for use on various roads and in rail transit has grown at great speed. These bridges in use are constantly subjected to erosive impacts from the environment, such as temperature changes, strong winds and rainfalls, plus repeated and long-term actions from vehicle loads and strikes, and parts of the bridges even suffer from natural disasters like floods, earthquakes and so on, so over years of use, different degrees of fatigue effects and aging phenomena commonly appear in the bridges. As bodies of the bridges accumulate a great deal of exterior damages, many of them have become “danger bridges” in a certain sense. Bridges of such kind, located worldwide, have had quite a few instances of collapse, which posed a great threat to the security of human lives and properties. Thus, it is significant to detect damages of bridge in real time and effectively.
From the perspectives of the implementation period and the detection accuracy, the bridge detection comprises mainly the two modes of periodic inspection and real-time inspection: for the periodic inspection, such as visual inspection with instruments, bridge dynamic or static load tests, etc., it yields high accuracy but runs at a long time interval, which is unfavorable to a timely discovery of bridge damage and requires also an interruption of the bridge traffic, thus is difficult to implement; for the real-time detection, such as some bridge health monitoring systems, it does well in real-time function but yields low accuracy and runs with a high cost, thus it is difficult for it to gain wide use in short time.
From the perspective of real operation, existing bridge damage identification techniques may be divided into two modes: off-line local detection and on-line overall monitoring. The off-line local detection refers to probing carefully the damage(s) in structure using nondestructive detection means such as the observation by naked human eyes, ultrasound waves, electromagnetic eddy current and X-rays, etc., when the bridge is not in operation. Such methods yield higher accuracy of detection, but require always an interruption of the traffic, thus affecting the normal service of the bridge, need to know an approximate position of the damage in advance, and suffer from dead corners and low efficiency of detection. With the pre-installed sensor network, the on-line monitoring acquires bridge response signals in real time so as to infer the status of the damage(s). While such methods would not require an interruption of the bridge traffic, the detection accuracy is low, and there further exist issues such as installation of the sensor, transmission and storage of massive signals, and anti-noise capability and durability of the sensor.